DK Panda from Ohio State University gave this talk at the Stanford HPC Conference. “This talk will focus on a range of solutions being carried out in my group to address these challenges. The solutions will include: 1) MPI-driven Deep Learning, 2) Co-designing Deep Learning Stacks with High-Performance MPI, 3) Out-of- core DNN training, and 4) Hybrid (Data and Model) parallelism. Case studies to accelerate DNN training with popular frameworks like TensorFlow, PyTorch, MXNet and Caffe on modern HPC systems will be presented.”
How to Achieve High-Performance, Scalable and Distributed DNN Training on Modern HPC Systems
May 3, 2020 by
GIGABYTE Brings AI and Cloud Solutions to CES 2020
January 6, 2020 by
GIGABYTE is showcasing AI, Cloud, and Smart Applications this week at CES 2020 in Las Vegas. “GIGABYTE is renowned for its craftsmanship and dedication to innovating new technologies that are current with the time and helping humanity leap forward for more than 30 years. GIGABYTE’s accomplishments in motherboards and graphics cards have set the standard for the industry to follow, and the quality and performance of its products have been the excellence that competitors look up to. GIGABYTE has leveraged the experience and know-how to establish a trusted reputation in data center expertise, and is responsible in supplying the hardware and support to some of the biggest companies involved in HPC and cloud & web hosting services, enabling their successes in the respective fields.”
Neurala Reduces Training Time for Deep Neural Network Technology
May 11, 2018 by
Today Neurala announced a breakthrough update to its award-winning Lifelong Deep Neural Network (Lifelong-DNN) technology. The update allows for a significant reduction in training time compared to traditional DNN—20 seconds versus 15 hours—a reduction in overall data needs, and the ability for deep learning neural networks to learn without the risk of forgetting previous knowledge—with or without the cloud. “It takes a very long time to train a traditional DNN on a dataset, and, once that happens, it must be completely re-trained if even a single piece of new information is added. Our technology allows for a massive reduction in the time it takes to train a neural network and all but eliminates the time it takes to add new information,” said Anatoli Gorshechnikov, CTO and co-founder of Neurala. “Our Lifelong-DNN is the only AI solution that allows for incremental learning and is the breakthrough that companies across many industries have needed to make deep learning useful for their customers.”
DNN Implementation, Optimization, and Challenges
May 7, 2018 by
This is the third in a five-part series that explores the potential of unified deep learning with CPU, GPU and FGPA technologies. This post explores DNN implementation, optimization and challenges.
Exploring the Possibilities of Deep Learning Software
April 30, 2018 by
This is the second post in a five-part series from a report that explores the potential of unified deep learning with CPU, GPU and FGPA technologies. This post explores the possibilities and functions of software for deep learning.
Insilico Applies Deep Learning to Drug Discovery
May 27, 2016 by
“The world of artificial intelligence is rapidly evolving and affecting every aspect of our daily life. And soon this progress will be felt in the pharmaceutical industry. We set up the Pharma.AI division to help pharmaceutical companies significantly accelerate their R&D and increase the number of approved drugs, but in the process we came up with over 800 strong hypotheses in oncology, cardiovascular, metabolic and CNS space and started basic validation. We are cautious about making strong statements, but if this approach works, it will uberize the pharmaceutical industry and generate unprecedented number of QALY,” said Alex Zhavoronkov, PhD, CEO of Insilico Medicine, Inc.
